{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cde15aed-b753-4699-bbf8-6c1407107390",
   "metadata": {},
   "source": [
    "### 任务清单\n",
    "1. 订单表的长度\n",
    "2. 菜名平均价格\n",
    "3. 什么菜最受欢迎\n",
    "4. 哪个订单ID点菜最多\n",
    "5. 哪一天订单数量最多\n",
    "6. 一天中哪个时段订单数最多\n",
    "7. 查看星期几人数最多，订餐数最多\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7b51c844-cb7b-4bb8-9355-71ee8fc2e67b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10037 entries, 0 to 3610\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   detail_id          10037 non-null  int64         \n",
      " 1   order_id           10037 non-null  int64         \n",
      " 2   dishes_id          10037 non-null  int64         \n",
      " 3   logicprn_name      0 non-null      float64       \n",
      " 4   parent_class_name  0 non-null      float64       \n",
      " 5   dishes_name        10037 non-null  object        \n",
      " 6   itemis_add         10037 non-null  int64         \n",
      " 7   counts             10037 non-null  int64         \n",
      " 8   amounts            10037 non-null  int64         \n",
      " 9   cost               0 non-null      float64       \n",
      " 10  place_order_time   10037 non-null  datetime64[ns]\n",
      " 11  discount_amt       0 non-null      float64       \n",
      " 12  discount_reason    0 non-null      float64       \n",
      " 13  kick_back          0 non-null      float64       \n",
      " 14  add_inprice        10037 non-null  int64         \n",
      " 15  add_info           0 non-null      float64       \n",
      " 16  bar_code           0 non-null      float64       \n",
      " 17  picture_file       10037 non-null  object        \n",
      " 18  emp_id             10037 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(8), int64(8), object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "#导入数据\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "data1 = pd.read_excel('data/meal_order_detail.xlsx',sheet_name='meal_order_detail1')\n",
    "data2 = pd.read_excel('data/meal_order_detail.xlsx',sheet_name='meal_order_detail2')\n",
    "data3 = pd.read_excel('data/meal_order_detail.xlsx',sheet_name='meal_order_detail3')\n",
    "\n",
    "#数据合并\n",
    "data = pd.concat([data1,data2,data3],axis=0)\n",
    "\n",
    "data.head(5)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "820597a7-6c71-4889-b42c-6841f4d0e4c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10037 entries, 0 to 3610\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   detail_id         10037 non-null  int64         \n",
      " 1   order_id          10037 non-null  int64         \n",
      " 2   dishes_id         10037 non-null  int64         \n",
      " 3   dishes_name       10037 non-null  object        \n",
      " 4   itemis_add        10037 non-null  int64         \n",
      " 5   counts            10037 non-null  int64         \n",
      " 6   amounts           10037 non-null  int64         \n",
      " 7   place_order_time  10037 non-null  datetime64[ns]\n",
      " 8   add_inprice       10037 non-null  int64         \n",
      " 9   picture_file      10037 non-null  object        \n",
      " 10  emp_id            10037 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(8), object(2)\n",
      "memory usage: 941.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#删除空值列\n",
    "data.dropna(axis=1,inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6977ccea-2134-4f11-81a9-b1b65999159c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(44.82)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计卖出菜品的平均价格\n",
    "#round(data['amounts']`.mean(),2) # 方法一\n",
    "round(np.mean(data['amounts']),2) #方法2 nump函数处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f08e5c50-17c0-4e29-8ce0-f1f0f880026b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dishes_name\n",
      "白饭/大碗        323\n",
      "凉拌菠菜         269\n",
      "谷稻小庄         239\n",
      "麻辣小龙虾        216\n",
      "辣炒鱿鱼         189\n",
      "芝士烩波士顿龙虾     188\n",
      "五色糯米饭(七色)    187\n",
      "白饭/小碗        186\n",
      "香酥两吃大虾       178\n",
      "焖猪手          173\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#频数统计，什么菜最受欢迎\n",
    "dishes_count = data['dishes_name'].value_counts()[:10]\n",
    "print(dishes_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c13354a5-77ce-4b2f-ae77-05bd9af46a97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 323\n",
      "1 269\n",
      "2 239\n",
      "3 216\n",
      "4 189\n",
      "5 188\n",
      "6 187\n",
      "7 186\n",
      "8 178\n",
      "9 173\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据可视化\n",
    "\n",
    "dishes_count.plot(kind='line',color=['r'])\n",
    "dishes_count.plot(kind='bar',fontsize=16)\n",
    "for x,y in enumerate(dishes_count):\n",
    "    print(x,y)\n",
    "    plt.text(x,y+2,y,ha='center',fontsize=12)\n",
    "plt.title(\"菜品售卖排行榜\")\n",
    "plt.ylabel(\"数量\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6fa55c4a-a3a3-4d99-bbc1-1fe99b7178a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_id\n",
      "137      6\n",
      "162     15\n",
      "163      5\n",
      "165     18\n",
      "166      5\n",
      "        ..\n",
      "1320     1\n",
      "1321     7\n",
      "1322    12\n",
      "1323    15\n",
      "1324    13\n",
      "Length: 942, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "order_id\n",
       "398     36\n",
       "1295    29\n",
       "1078    27\n",
       "465     27\n",
       "582     27\n",
       "1311    26\n",
       "1033    25\n",
       "672     24\n",
       "1166    24\n",
       "1318    24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#订单点菜总类最多\n",
    "data_group = data.groupby(by='order_id').size().sort_values(ascending=False)[:10]  # 方法一 使用group分组\n",
    "#data_group = data['order_id'].value_counts()\n",
    "data_group\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c4d2da26-420b-4280-8f40-3815435b250c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_group.plot(kind='bar',fontsize=16)\n",
    "plt.title('点菜种类top10订单')\n",
    "plt.xlabel('订单Id',fontsize=16)\n",
    "plt.ylabel('点菜总类',fontsize=16)\n",
    "plt.show()\n",
    "#8月份餐厅订单点菜种类前10名，平均点菜25个菜品"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cd8aff57-cca1-4450-a93b-7baf76be64ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>total_amounts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>36</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>33</td>\n",
       "      <td>1028</td>\n",
       "      <td>1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>33</td>\n",
       "      <td>730</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>31</td>\n",
       "      <td>1027</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>30</td>\n",
       "      <td>993</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>3</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>2</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>2</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          counts  amounts  total_amounts\n",
       "order_id                                \n",
       "398           36      980            980\n",
       "1033          33     1028           1083\n",
       "1051          33      730            835\n",
       "1318          31     1027           1076\n",
       "1078          30      993           1113\n",
       "...          ...      ...            ...\n",
       "798            3      127            127\n",
       "1035           2       95             95\n",
       "703            2      127            127\n",
       "1064           1       48             48\n",
       "1320           1       78             78\n",
       "\n",
       "[942 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#订单id定采数量最多\n",
    "data['total_amounts'] = data['counts']*data['amounts']\n",
    "data_group_temp = data[['order_id','counts','amounts','total_amounts']].groupby(by='order_id')\n",
    "group_sum = data_group_temp.sum()\n",
    "sort_counts = group_sum.sort_values(by='counts',ascending=False)\n",
    "sort_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c4828a2-c2f6-4fba-90d4-86c3357cfd17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_counts['counts'][:10].plot(kind='bar',fontsize=16)\n",
    "plt.xlabel('order id')\n",
    "plt.ylabel('order number')\n",
    "plt.title(' number of dishes by order id,top10 ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "697d2ddd-9c98-438b-830e-65c15e5b358f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 10 orders for consumption\n",
    "sort_total_amounts = group_sum.sort_values(by='total_amounts',ascending=False)\n",
    "sort_total_amounts['total_amounts'][:10].plot(kind='bar')\n",
    "plt.xlabel('order id')\n",
    "plt.ylabel('total amounts')\n",
    "plt.title('consumoption by order id ，top10')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "af7dfc97-93bb-417d-9d45-53e6ec8b4f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>total_amounts</th>\n",
       "      <th>average_amounts</th>\n",
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       "    <tr>\n",
       "      <th>order_id</th>\n",
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       "      <td>50.933333</td>\n",
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       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>13</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "      <td>33.692308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          counts  amounts  total_amounts  average_amounts\n",
       "order_id                                                 \n",
       "137            9      194            197        21.888889\n",
       "162           18     1032           1101        61.166667\n",
       "163           10      182            217        21.700000\n",
       "165           21      953           1147        54.619048\n",
       "166            7      241            260        37.142857\n",
       "...          ...      ...            ...              ...\n",
       "1320           1       78             78        78.000000\n",
       "1321           7      458            458        65.428571\n",
       "1322          13      547            635        48.846154\n",
       "1323          15      764            764        50.933333\n",
       "1324          13      438            438        33.692308\n",
       "\n",
       "[942 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#订单id平均单品消费最贵前10名\n",
    "group_sum['average_amounts'] = group_sum['total_amounts']/group_sum['counts']\n",
    "group_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5b1891ec-2c94-4460-9f43-7a1a3d61dad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41.98852379286684\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>total_amounts</th>\n",
       "      <th>average_amounts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>168</th>\n",
       "      <td>9</td>\n",
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       "      <td>7</td>\n",
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       "      <td>715</td>\n",
       "      <td>102.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>6</td>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>10</td>\n",
       "      <td>126</td>\n",
       "      <td>140</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>9</td>\n",
       "      <td>112</td>\n",
       "      <td>124</td>\n",
       "      <td>13.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>6</td>\n",
       "      <td>70</td>\n",
       "      <td>76</td>\n",
       "      <td>12.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          counts  amounts  total_amounts  average_amounts\n",
       "order_id                                                 \n",
       "168            9      423           1105       122.777778\n",
       "909            4      471            471       117.750000\n",
       "418            4      451            451       112.750000\n",
       "891            7      715            715       102.142857\n",
       "492            3      301            301       100.333333\n",
       "...          ...      ...            ...              ...\n",
       "1174           8      110            121        15.125000\n",
       "1256           6       77             84        14.000000\n",
       "632           10      126            140        14.000000\n",
       "1303           9      112            124        13.777778\n",
       "874            6       70             76        12.666667\n",
       "\n",
       "[942 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_average =group_sum.sort_values(by='average_amounts',ascending=False)\n",
    "print(sort_average['average_amounts'].mean())\n",
    "sort_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cd34326a-32df-4a1b-8602-782fa3101e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sort_average['average_amounts'][:10].plot(kind='bar')\n",
    "plt.title('average amounts group by order id,top10')\n",
    "plt.xlabel('order id')\n",
    "plt.ylabel('average amounts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bbd05bbc-aaec-4b6a-bb72-242476e2d1fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>dishes_id</th>\n",
       "      <th>logicprn_name</th>\n",
       "      <th>parent_class_name</th>\n",
       "      <th>dishes_name</th>\n",
       "      <th>itemis_add</th>\n",
       "      <th>counts</th>\n",
       "      <th>amounts</th>\n",
       "      <th>cost</th>\n",
       "      <th>...</th>\n",
       "      <th>kick_back</th>\n",
       "      <th>add_inprice</th>\n",
       "      <th>add_info</th>\n",
       "      <th>bar_code</th>\n",
       "      <th>picture_file</th>\n",
       "      <th>emp_id</th>\n",
       "      <th>daycount</th>\n",
       "      <th>hourcount</th>\n",
       "      <th>time</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2956</td>\n",
       "      <td>417</td>\n",
       "      <td>610062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜蓉生蚝</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/104001.jpg</td>\n",
       "      <td>1442</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-01 11:05:36</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2958</td>\n",
       "      <td>417</td>\n",
       "      <td>609957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒙古烤羊腿</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202003.jpg</td>\n",
       "      <td>1442</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-01 11:07:07</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2961</td>\n",
       "      <td>417</td>\n",
       "      <td>609950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>大蒜苋菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/303001.jpg</td>\n",
       "      <td>1442</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-01 11:07:40</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2966</td>\n",
       "      <td>417</td>\n",
       "      <td>610038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>芝麻烤紫菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/105002.jpg</td>\n",
       "      <td>1442</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-01 11:11:11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2968</td>\n",
       "      <td>417</td>\n",
       "      <td>610003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>蒜香包</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/503002.jpg</td>\n",
       "      <td>1442</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-01 11:11:30</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3606</th>\n",
       "      <td>5683</td>\n",
       "      <td>672</td>\n",
       "      <td>610049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>爆炒双丝</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/301003.jpg</td>\n",
       "      <td>1089</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-31 21:53:30</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>5686</td>\n",
       "      <td>672</td>\n",
       "      <td>609959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>小炒羊腰\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/202005.jpg</td>\n",
       "      <td>1089</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-31 21:54:40</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>5379</td>\n",
       "      <td>647</td>\n",
       "      <td>610012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>香菇鹌鹑蛋</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/302001.jpg</td>\n",
       "      <td>1094</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-31 21:54:44</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3609</th>\n",
       "      <td>5380</td>\n",
       "      <td>647</td>\n",
       "      <td>610054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>不加一滴油的酸奶蛋糕</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/501003.jpg</td>\n",
       "      <td>1094</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-31 21:55:24</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>5688</td>\n",
       "      <td>672</td>\n",
       "      <td>609953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>凉拌菠菜</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>caipu/303004.jpg</td>\n",
       "      <td>1089</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-31 21:56:54</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10037 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
       "0          2956       417     610062            NaN                NaN   \n",
       "1          2958       417     609957            NaN                NaN   \n",
       "2          2961       417     609950            NaN                NaN   \n",
       "3          2966       417     610038            NaN                NaN   \n",
       "4          2968       417     610003            NaN                NaN   \n",
       "...         ...       ...        ...            ...                ...   \n",
       "3606       5683       672     610049            NaN                NaN   \n",
       "3607       5686       672     609959            NaN                NaN   \n",
       "3608       5379       647     610012            NaN                NaN   \n",
       "3609       5380       647     610054            NaN                NaN   \n",
       "3610       5688       672     609953            NaN                NaN   \n",
       "\n",
       "           dishes_name  itemis_add  counts  amounts  cost  ... kick_back  \\\n",
       "0                 蒜蓉生蚝           0       1       49   NaN  ...       NaN   \n",
       "1                蒙古烤羊腿           0       1       48   NaN  ...       NaN   \n",
       "2                 大蒜苋菜           0       1       30   NaN  ...       NaN   \n",
       "3                芝麻烤紫菜           0       1       25   NaN  ...       NaN   \n",
       "4                  蒜香包           0       1       13   NaN  ...       NaN   \n",
       "...                ...         ...     ...      ...   ...  ...       ...   \n",
       "3606              爆炒双丝           0       1       35   NaN  ...       NaN   \n",
       "3607  小炒羊腰\\r\\n\\r\\n\\r\\n           0       1       36   NaN  ...       NaN   \n",
       "3608             香菇鹌鹑蛋           0       1       39   NaN  ...       NaN   \n",
       "3609        不加一滴油的酸奶蛋糕           0       1        7   NaN  ...       NaN   \n",
       "3610              凉拌菠菜           0       1       27   NaN  ...       NaN   \n",
       "\n",
       "      add_inprice  add_info  bar_code      picture_file  emp_id  daycount  \\\n",
       "0               0       NaN       NaN  caipu/104001.jpg    1442         1   \n",
       "1               0       NaN       NaN  caipu/202003.jpg    1442         1   \n",
       "2               0       NaN       NaN  caipu/303001.jpg    1442         1   \n",
       "3               0       NaN       NaN  caipu/105002.jpg    1442         1   \n",
       "4               0       NaN       NaN  caipu/503002.jpg    1442         1   \n",
       "...           ...       ...       ...               ...     ...       ...   \n",
       "3606            0       NaN       NaN  caipu/301003.jpg    1089         1   \n",
       "3607            0       NaN       NaN  caipu/202005.jpg    1089         1   \n",
       "3608            0       NaN       NaN  caipu/302001.jpg    1094         1   \n",
       "3609            0       NaN       NaN  caipu/501003.jpg    1094         1   \n",
       "3610            0       NaN       NaN  caipu/303004.jpg    1089         1   \n",
       "\n",
       "     hourcount                time  hour  \n",
       "0            1 2016-08-01 11:05:36    11  \n",
       "1            1 2016-08-01 11:07:07    11  \n",
       "2            1 2016-08-01 11:07:40    11  \n",
       "3            1 2016-08-01 11:11:11    11  \n",
       "4            1 2016-08-01 11:11:30    11  \n",
       "...        ...                 ...   ...  \n",
       "3606         1 2016-08-31 21:53:30    21  \n",
       "3607         1 2016-08-31 21:54:40    21  \n",
       "3608         1 2016-08-31 21:54:44    21  \n",
       "3609         1 2016-08-31 21:55:24    21  \n",
       "3610         1 2016-08-31 21:56:54    21  \n",
       "\n",
       "[10037 rows x 23 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#一天当中什么时间点菜量比较集中\n",
    "data['hourcount'] = 1 # 添加新列 用作计数\n",
    "data['time'] = pd.to_datetime(data['place_order_time'])\n",
    "data['hour'] = data['time'].map(lambda x:x.hour)\n",
    "data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "60ecccbe-a031-4304-a920-39b4df77dd8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "18    1564\n",
       "20    1531\n",
       "21    1469\n",
       "19    1464\n",
       "17    1092\n",
       "11     960\n",
       "12     842\n",
       "13     823\n",
       "22     175\n",
       "14     117\n",
       "Name: hourcount, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_hour = data.groupby(by='hour').count()['hourcount'].sort_values(ascending=False)\n",
    "group_by_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f92ea3db-04b4-4306-8ad6-5c64bced2e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_by_hour.plot(kind='bar')\n",
    "plt.xlabel('hour')\n",
    "plt.ylabel('order number')\n",
    "plt.title('relationship between order number and  hour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "29631cf0-9c2a-455b-9d52-faf107603bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C3/nz502FhOs4Lvk90LguM2mYIdAAAGAXb5qL0CgYAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAYL3wQBcAAOCb6mNWZjj8yLTOuVYWIFhQQwMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPXCA10AAADgvepjVmY4/Mi0zpIfBaSGZu7cuVKlShWJiIiQW2+9VQ4fPmxej4uLk5iYGClVqpSMGjVKHMdxj7Nx40apW7eulC1bVmbMmBGIYgMAgCCV64Hm0KFD8sILL8iyZctk3759UrNmTendu7ckJydLly5dpGnTprJ9+3bZs2ePLFiwwIxz6tQp6dq1q/To0UO2bNkiCxculPXr1+d20QEAQJDK9UCzc+dOad68uTRp0kSqVq0qffv2lYMHD8qqVavk3LlzpvZFQ86UKVNk3rx5ZhwNMJUqVZJx48ZJ7dq1Zfz48e5hAAAAuR5o6tWrJ+vWrZNvv/3WBJjZs2fLHXfcIbt27TJBRy9DqYYNG5paGqXD2rVrJyEhIeZ5bGys7NixI91paG3P+fPnUzwAAEDeFZBA88ADD0jjxo2lZMmS5hLSyy+/bEJHdHS0+30aXsLCwuTMmTPXDIuMjJTjx4+nO42pU6dKiRIl3A9trwMAAPKuXA8027ZtkxUrVsjWrVvl7Nmzpl1Mp06dJDw8XAoVKpTivYULF5akpKRrhrleT8/YsWNN7Y/rkZCQkKPzBAAA8lmg+eCDD+Thhx+Wm2++2dSeTJ482TQULl26tGn86ykxMVEKFix4zTDX6+nR8KO1OJ4PAACQd+V6oLl69ar8/PPPKcKJqxZGLz+5xMfHm7YwGma0K7fnMG1YHBUVldtFBwAAQSrXA03r1q1l6dKlMnPmTFm0aJHcc889UqFCBXnqqadMW5n58+eb92kvp/bt25t2NNple/PmzbJmzRq5cuWKTJ8+XTp06JDbRQcAAEEq1+8UfP/998vevXtl1qxZcuLECWnQoIF89NFHUqBAAXPDPW1TozfVCw0NlQ0bNphx9GZ6GoC0rU2xYsVMY2LXPWoAAABCHM/b8QaBkydPmi7Z2oW7TJkyKYbpZSi9GZ/W8miw8ZbW/Gh7HW0gTHsaALbj1vf5W35a/+d9OH4H3W856eWnzp3TXhnadduz+zYAAIDi17YBAID1gq6GBgCQs/LTJQvkH9TQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADW48Z6QD7EjdUA5DXU0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAevyWEwAAuYjfUssZ1NAAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKzHfWiQJdxHAQAQTKihAQAA1iPQAAAA6xFoAACA9WhDg4CgDQ4AwJ+ooQEAANajhgaAz6hhAxBsqKEBAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALBeQAPN6NGjpUuXLu7ncXFxEhMTI6VKlZJRo0aJ4zjuYRs3bpS6detK2bJlZcaMGQEqMQAACEYBCzS7d++W2bNny1//+lfzPDk52YSbpk2byvbt22XPnj2yYMECM+zUqVPStWtX6dGjh2zZskUWLlwo69evD1TRAQBAkAlIoLl69aoMGDBAhg0bJjVq1DCvrVq1Ss6dO2dqX2rWrClTpkyRefPmmWEaYCpVqiTjxo2T2rVry/jx493DAAAAAhJo5syZI999951Ur15dli9fLpcvX5Zdu3ZJ8+bNJSIiwrynYcOGppZG6bB27dpJSEiIeR4bGys7duwIRNEBAEAQyvVAc+HCBZkwYYKpmfnhhx9k5syZ0qpVKzl//rxER0e736fhJSwsTM6cOXPNsMjISDl+/Hi609DLVzqO5wMAAORduR5oli5dKr/99ptpAzNp0iT54osvJDExUd555x0pVKhQivcWLlxYkpKSJDw8PMUw1+vpmTp1qpQoUcL9qFKlSo7OEwAAyGeB5tixY+bSkvZWUhpW9PLS2bNnTeNfTxp0ChYsKKVLl04xzPV6esaOHWva47geCQkJOThHAAAg3wWaypUry8WLF1O8ppeeZs2aZXowucTHx5tLRxpmtCu357CdO3dKVFRUutPQ2hy9LOX5AAAAeVeuB5rOnTubxr7aMFhra1599VXT6Pe+++4zbV3mz59v3qe9nNq3b2/a0WiX7c2bN8uaNWvkypUrMn36dOnQoUNuFx0AAASp8NyeYJkyZeTTTz+VkSNHyvDhw6VixYqyePFi085l7ty55l4zelO90NBQ2bBhgxlHL09p4+FOnTpJsWLFpGTJku571AAAAOR6oFEtW7ZMcQnJRWtiDh06ZLpkazsbDT8ugwYNMrUy+/btk9atW5tgAwAAELBAk5EKFSqYy1Jp0a7bnt23AQAAFD9OCQAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAACQ/wLN3r17r/npAgAAAKsCze233y5ff/11zpQGAAAgNwJNr1693L+3BAAAYGWg0R+MPHDggLmb76pVq+TLL790PwAAAKz46YN+/fqZ/0+cOCGDBw92vx4SEiKHDx/2b+kAAAByItDEx8f7OgoAAECOots2AADIf4HmypUrMmXKFLn55pslKipK/u///k9iY2Pl0KFDOVNCAAAAfwcabTezePFi6du3ryQmJkpERIS0aNFCBg4c6OtHAQAABCbQfPjhh7JkyRITYMLCwsxj9OjR3JsGAADYE2iqVKmSoou29m7Sy07R0dH+LhsAAEDO9HKaPn263HPPPfLWW29JUlKSDB8+XDZt2iTvvfeerx8FAAAQmEDTsWNHiYuLM+1oGjduLJUrV5aXXnpJatSo4Z8SAQAA5HSgUbVq1ZJnn302K6MCAAAEvg2N/tL2mDFjTKgpWrSoqZnRy07a4wkAAMCKQDNgwABZu3atucy0YcMGmTFjhmzdutW8DgAAYMUlp+XLl8uOHTtMDY2KiYmRBg0amP8BAACsqKFp1qyZfPXVVyle27ZtmzRq1Mif5QIAAPBvDY3eFdilePHi0rt3b5kzZ46590xCQoJs3rzZ9H4CAAAI2kBTrVq1FH9rd22XOnXqyO23325usAcAABC0gWbChAnuv7X9zOeff25+pBIAAMDKRsEdOnRw31APAADAykATGxsrTzzxhHTp0iVnSgQAAJDTgaZevXrSrVs3qVChghQuXDjFsMOHD/v6cQAAALkfaObOnWt+mLJ27drZnzoAAEAg7kPTo0cPc4fg5ORk07PJ8wEAAGBFDc3q1avN/3rvGU8aaLjkBAAArAg08fHxOVMSAACA3Ao0oaGh6V5e+uOPP7JaDgAAgMDU0CQlJcn27dvlL3/5izz33HNZLwUAAEBuBhrPn0FQdevWNb/jpPeleeihh7JTFgAAgNzp5ZSWIkWKyMmTJ/3xUQAAADlfQ9OuXbsUbWiuXr0qe/bskTvvvNP3qQMAAAQi0PTu3TvFcw03UVFRctttt/mjPAAAADkfaHr16uX7VAAAAIKpDc3HH38s1atXl7CwMPdDu3Lr/wAAAFbU0AwaNEj69Okjffv2lYIFC+ZMqQAAAHIy0Kj+/ftLjRo1sjIqAABA4C85TZ48WZ588kn59ddf/V8aAACA3KihWbhwocTFxUnVqlXNTfUiIyPdw9atW5eVMgAAAAS22zYAAECg0W0bAABYzy8/fQAAABBIBBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAgPwXaFavXi1nzpzJmdIAAADkRqDRX9vevn17VqYFAAAQHIHmqaeeklmzZsnvv/+eMyUCAADI6Z8+KFOmjLnk1LRpU1NbU7RoUfewnj17+vpxAAAAuR9oFixYIIUKFTKPxYsXu18PCQkh0AAAADsCzfr163OmJAAAAIHotn358mVxHEeuXr2anY8BAADI3UCTmJgoAwYMkOuuu04iIiJk9+7dUrlyZdmxY0f2SgIAAJBbgaZPnz5y5MgReffdd02D4JIlS8qwYcNkyJAhWS0DAABA7rahWbNmjXz33XdSpUoVCQ0NNY2Be/XqJS+88EL2SgIAAJBbNTQ33HCD6emkNMzo48svv5T69etntQwAAAC5W0Pz2muvSadOnWTOnDmmPU337t3l6NGjsnz58uyVBAAAILcCTUxMjBw8eFA++eQT+fHHH02D4M6dO0uJEiWyWgYAAIDc77at4eXRRx+VZ555Rh555JFshZmOHTu6L2Ft3LhR6tatK2XLlpUZM2akeN+HH34o1apVk0qVKskHH3yQ5ekBAIB8Gmi08W9YWFimD18tXLhQPvvsM/P3qVOnpGvXrtKjRw/ZsmWLGea6iV9cXJwJUOPGjTPvHz9+vOzfv9/n6QEAgHwcaOLj4+Xw4cPmMXHiRGnWrJm55LR3715ZvXq13HLLLTJq1CifJnz69GkZMWKEXH/99ea5BhitfdHQUrt2bRNa5s2bZ4bNnTtX2rVrJ/369ZMbb7xRhg4dKu+//35W5hcAAOTXNjR6qcezUfDWrVulRo0a5nmdOnWkZs2a0rp1a5k2bZrXE9Ywc++998rFixfN8127dpnQor2mVGxsrIwZM8Y97K677nKPq8My6iaenJxsHi7nz5/3ulwAACAftKHR9jIbNmxI8drmzZulcOHCXn+GXkpau3atTJ8+PUXoiI6Odj+PjIyU48ePZzosLVOnTjXldD30njkAACDv8rmX08yZM+Xhhx+WWbNmSdWqVU2w2Ldvn7lk5I1Lly7JwIED5Y033pDixYv/tyDh4eYXvF00ICUlJWU6LC1jx46V4cOHu59rICLUAACQd/kcaO6++27Tlkbbzpw4cULKly8vd955p0RFRXk1/p///GfT9Vu7ensqXbq0aRjsove4KViwYKbD0qLhxzMAAQCAvM3nQKM0xPTs2TNLE1y0aJEJJ/obUEprWhYvXmz+btGihft9O3fudIckDUDa8+nxxx+/ZhgAAIDPbWjefPNNUzOTVZs2bTLdsL/99lvz0K7a2sBX7zasbXH0t6KuXLli2td06NDBjHP//ffLP/7xD/MbUhcuXJBXX33VPQwAACBLbWi0PUrFihWzNEG9s7CnYsWKmRvp6UM/W39WQV/TGhzXDfduuukmefrpp013cW0/o926Bw8enKXpAwCAvMfnQKP3iZk8ebK0adPGBI/scoUWNWjQIFPzoo2MtRu45+e/+OKL5uZ6+nMLbdu2zbANDQAAyF98DjT6O06qVq1a0qtXLylatKh7mN4ML7u0e7ZnF21P9erVMw8AAIBsBZojR46Yu/vq4+eff3a/7rohHgAAQNAHmvnz5+dMSQAAAHKz27batm2b6ZlUvXp101gXAADAmkCjjXK7desm33//vbkXjN4pWH/PadmyZebHJQEAAIL+PjT6swVaI6M3x9Nf29Z2NE2aNJH+/fvnTAkBAAD8XUPz73//29zgzvXTAvr/c889Jw0bNvT1owAAAAJTQ3PjjTfKu+++m+I1fd6gQQP/lAgAACCna2j0V7L15nf669p6v5j4+HjzY5GfffaZrx8FAAAQmECjNTHaIHjFihWSkJBgbq6nv8DteYM9AACAoO+2reHl4Ycf9n9pAAAAcqMNDQAAQLAh0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPXCA10AAMhvqo9ZmeHwI9M651pZgLyCGhoAAGA9amiALOAMGwCCCzU0AADAevm2hoYzbAAA8o58G2gAIKs4IQKCT0AuOS1btkxq1Kgh4eHh0qhRI9m7d695PS4uTmJiYqRUqVIyatQocRzHPc7GjRulbt26UrZsWZkxY0Ygig3Aj4EgowcABH2gOXTokPTp00emTZsmP/74o9SpU0f69esnycnJ0qVLF2natKls375d9uzZIwsWLDDjnDp1Srp27So9evSQLVu2yMKFC2X9+vW5XXQAABCkcv2Sk9bGaJjp3r27ef7EE09I586dZdWqVXLu3DlT+xIRESFTpkyRIUOGmPCjAaZSpUoybtw4CQkJkfHjx8u8efOkXbt2uV18AHkAl4yAvCfXA83dd9+d4vn+/fuldu3asmvXLmnevLkJM6phw4amlkbpMA0vGmZUbGysjBkzJt1paG2PPlzOnz+fQ3MDAAAkvzcKvnz5srzyyisyfPhwOXjwoERHR7uHaXgJCwuTM2fOmEBSr14997DIyEg5fvx4up87depUmTRpUo6XHwCQ/1DDF5wCeh+aCRMmSNGiRU0bGm0gXKhQoRTDCxcuLElJSdcMc72enrFjx5rLV65HQkJCjs4HAADIpzU069atk9dff122bt0qBQoUkNKlS5teTp4SExOlYMGCZpg2DE79eno0/KQORwAAIO8KSKCJj483PZY00LguJWl37bfffjvFe7QdjIYZHbZo0SL3sJ07d0pUVFQgip5nUGUKAMhLcv2S08WLF03D4G7dusm9994rFy5cMI/WrVubtjLz588379NeTu3btzftaLTL9ubNm2XNmjVy5coVmT59unTo0CG3iw4AAIJUrtfQfP7556b3kj5S18jMnTvX1NzoTfVCQ0Nlw4YNZpjeTG/mzJnSqVMnKVasmJQsWdJ9jxoAAIBcDzRaM+N5B2BP1atXNzfe27Fjh+nCXaZMGfewQYMGmVqZffv2mdocDTYAAMAuOdXkIeh+y6lChQrmRntp0W7dnl27AQAAgjLQAADyNjolICcQaAALcUAAgJQINEAAEEgAIA/dKRgAAMAfCDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPXk6wEr2EAACeCDQBwgEZAAD/4ZITAACwHjU0AADA+isM1NAAAADrUUMDAEA+Uj1Ia1iyi0CDfCmvfqGB/PD9CfT0EZwINJbiCw0AwH/RhgYAAFiPGposooYEAIDgQaABAOQrnJDmTVxyAgAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6/DglAOvw44IAUqOGBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrWRVo4uLiJCYmRkqVKiWjRo0Sx3ECXSQAABAErAk0ycnJ0qVLF2natKls375d9uzZIwsWLAh0sQAAQBCwJtCsWrVKzp07JzNmzJCaNWvKlClTZN68eYEuFgAACALhYoldu3ZJ8+bNJSIiwjxv2LChqaVJrzZHHy4ahNT58+fdr11NTspwep7vTQvjMz7jMz7jMz7j5+z4rr+9aWIS4ljSEGXEiBFy6dIlef31192vlStXTr7//nvTpsbTxIkTZdKkSQEoJQAA8LeEhASpXLly3qihCQ8Pl0KFCqV4rXDhwpKUlHRNoBk7dqwMHz7c/fzq1aty+vRpKVOmjISEhFzz2ZoAq1SpYhZYZGSkz2VjfMZnfMZnfMZn/Ei/j691LomJiVKpUqVMP8uaQFO6dGnTy8mTzmTBggWvea8Gn9Thp2TJkplOQxdmVlYI4zM+4zM+4zM+40fmyPglSpTIW42Ctbv2li1b3M/j4+NNOxkNOgAAIH+zJtC0adPGVE3Nnz/fPNdeTu3bt5ewsLBAFw0AAASYVW1o5s6dKz169DA31QsNDZUNGzb45bP18tSECROuuUzF+IzP+IzP+IzP+ME7vpW9nFxOnjwpO3bsMF24tZEvAACAdYEGAADA2jY0AAAA6SHQAAAA6xFoAACA9Qg0fnb06NEsjadd0i9cuJDl6Q4dOtTcDdlXOs0DBw6Yn5XICr0XkPY+GzNmjLz22mvy448/Zvj+9957z9xDyJ92794tO3fulD/++MOr9589e9bclBEAkHcQaHywf/9+ad26tRQrVkwaN24sb731VoqD6G+//SbR0dEZfsaSJUvMe/TOh3369DFB5sEHHzQ/36CPu+++W3799dd0w0B6jwULFpiH/p2eOnXqmDIqvc30XXfdZe6gfP3110vx4sWlX79+cvHiRa+Xx5UrV8z9gfQX0I8cOSKLFi0y0/j000/THad3796mh9qgQYN8Dn8ahNq2bWt+z6Nv375mOcXGxkq7du2kRYsWUrt2bRNu0qPv1/Jq7zidby2Hlpl28bCV/vDuiRMnzLYdDNtxVk7osnsyF6gTumA4mQv0Cd25INv+tBDwUuPGjZ3u3bs769evd9544w2ndu3a5rV9+/aZ4RcuXHBCQkzHsTSdOXPGKVasmLNgwQJn9+7dTp8+fZxKlSo5bdu2deLj451jx445vXr1ch566KE0x+/YsaP5/Lp165pxe/fu7X4ULFjQeeCBB8zr6dFxz5075/6su+++2zl69Khz6dIlZ926dc7111/vDBs2LMNl0LVrV+fIkSPm7+XLlzvt2rVzfv/9d/fwv/71r06tWrUyLIPO54svvuhcd911Trdu3ZxVq1al+Iz06LQef/xxZ/Pmzc6jjz7q1KxZ03nuuefMMJ0vHd6sWbN0x3/kkUeczp07O3v27DHrbMyYMaY89erVM/OSVWfPnnWOHz/u/PLLL87Vq1edQPvhhx98HkeXX2JiYramO2TIEOfXX3/1eTyd7vfff+9cvHjR53F123377bed0aNHO6+++qrZtjLy7rvvOocPH3b8bdeuXc7//u//erUdu/YF58+fz9K0dP/RqlUrp3Tp0k758uWdKlWqOCVLlnQiIiLM93Pv3r1OTtDvjE63aNGiTqNGjZw333wzxfzq/i80NDTd8T/88EOnevXqTmRkpNln6Tan+ywdJzw83Hw39TuU0bpL76FleuWVV8zf6dH9tZZR6X5P94FhYWFmH6DT131LUlKSV8vi8uXLTmxsrNkX6/66efPmZvmvXLky3XF0Orq+Bg4cmKXvqG63bdq0caKiosx+XpdVTEyM2Q4KFy7sREdHm+0wPfr+1q1bm+Wtj5tvvtlZuHChz/usQG1/3sh3gUY3JG8eadGN/ueff06xUetBsUSJEmbDyOwL/fXXXzsNGzZMMX6ZMmXMjtAlISHBfF56/v73vzsVKlRwBg8e7P5yKt2gMvuSaNlcO9EiRYo4Bw8eTDH8s88+c8qWLZvhZzzzzDNmhzRx4kRn/vz55n9PekDRnYs3oUoPRu+8847ZMehy0DCnn7l///40x9UvjC4fV4jwnB+1aNEip1ChQulOu1SpUu7xXW655RZn0KBB5oupwXLHjh2ONziocFAJxEFFv396IpLWNLRs/fr1MycKp0+f9uu+L7sndNk9mQuGE7pAnswFywndM9nY/vyxDWYm3wUa3eG4dia6MtN6pHdAqFGjhjkgpLZ27VqzEnUnmdHBRA/CetCLi4tzv5Z6w9Czzfr162c4D7qx6Be3WrVq5gvhbaDReVu9erXz22+/mS+eBqzU86Gfkxktsx48dAelAc1z49UDrIaE9Ojyce1UPB04cMCZMmWKOVjogSEtGhq+/fZb87f+r/PzzTffuIfPnDnTHKzT06BBAzOPLnoQ0R3yoUOHzEF02rRpmQY6xUGFg0qgDioanDKrYdJt+NNPP/Xrvi+7J3T+OJkL9AldIE/mguWErnQ2tj9/bIOZyXeB5tSpU+Zg/Nprr/k87scff2xWqIaO1HQl604ps5WxePFip1y5cmbjS23UqFHmC71mzRqvyqMHgDp16jiPPfaYU7x48Uy/0E8++aTTokULMw19v55Zumh5NLA9++yzXk1bw8Drr79u5kW/xLfffrv5bN2xfPXVV159qdOT3qUHXe5Vq1Y1B1ydrn4RK1eu7IwcOdLp2bOnKcfkyZPT/Vzdgeh8P/XUU87LL7/s3HrrrSZApT7oZ4aDCgeVQB1U9Duryz2974iGVZ3/n376ya/7vuye0PnrZC7QJ3SBOpkLlhO6mGxsf/7YBjOT7wKNa6G2b9/efabnC135n3zySZrDdCUvW7Ys08/QjTr1Tk1t3LjROXHihE/l0Z2yhhA9UP74449ejaNnozt37jQHQM8wpQcqX+kBTS8x6Bfyvffey7QNhW7wV65ccbJKl7/uVPUMV+lOSc/on3766TR3tql98cUXJvx06tTJef75582O1lccVDioBOqg8t1335nP1wCmNT1Dhw51RowYYbZpXZ66DWRU05OdfV92T+j8eTIXyBO6QJzMpT6h020kuyd0bdu29fmELrPtT0N5ZjWN2dkGM5MvAw2QHfql1gDJQSV/H1QCVUuYnJxstoNJkyaZ7U4bRL/00ktmXejJQnbaIGQ2rgav9Noo6XLThtkZ0WWfVsNt18mcr2X3PKHTz/Vm/PRO6N5//32fpp/WyVxG43tzMufN8l+yZIm7jZ7nCd2//vWvTPcp+t3W7VT3W+PGjbvmhM6b+U+9/WkN8fTp050VK1Z4fek2JzolKAINkAX6pf7oo4/SPKhk9UudW7WE/qwhDHQtYW7XEAa6ljB1o/A5c+Z43Sg8uw3K89r4viw7f0xfQ0ig579ly5bmsmlW5l/p9q3fc73sq5dofe1UkN1OCZkh0ACAJTwbhesByZdG4dm97QTj553x5/i47XjbqUCDenqdCvzRKSEz/No2kEM3D6tatapfx2V8xi9QoIAcP35cypUr57655fjx4+WNN96Q2bNnS7du3SQyMjLNm6xlZ1zGZ/xt27ZJ//79ZdeuXe7xK1asKF988YW50aw6duyYNGjQwNy8z9/jeyXLUQjIp7LTUym7vZwYP3+Pn51G4dm97QTj5+/xz2azU4E/OyWkh0AD+Cg7PZWy28uJ8fP3+NlpFJ7dBuWMn7/H90enAn93SkiNQANkQXZ6KmW32yLj5+/xs9MoPLsNyhk/f4/vj04F/u6U4Ik2NAAAwHr82jYAALAegQYAAFiPQAMAAKxHoAFgjSNHjkhISEigiwEgCBFoAACA9Qg0AADAegQaAEHtk08+kVq1aknZsmXlvffec7++efNmc8v0iIgIiY2NlT179pjX33//falfv777fRcuXJAiRYrIvn37AlJ+ALmDQAMgaP3000/y0EMPyejRo2Xr1q0m3KirV6/KAw88IPfdd58cPnxY2rRpIyNHjjTD9DdpDh06JPv37zfPV61aJXXq1JEbbrghoPMCIGcRaAAErdWrV0t0dLT5UTutpZk4caJ72M6dO+WZZ56RhIQE82N2rgCjP7DXsWNHWbJkiXn+8ccfm1AEIG8j0AAIWidOnEjxy9M1a9Y0/4eGhsrMmTMlKipKhgwZImfOnEnxK8Hdu3eXpUuXml/0XblyJYEGyAcINACCVvny5eX48ePu50ePHjX/b9iwQebOnWvazWzbtk0ef/zxFON17drVDJs/f76p2XEFIQB5F4EGQNC68847TWPed99917SLcV1ySkxMNP/rpSZtHDx8+HD9oV33eMWKFZO77rrLXJKidgbIHwg0AIJW5cqVZeHChTJp0iRp1aqVtGzZ0ryubWT00aRJExk0aJBpY6M1OdqI2EWDzLlz58zlJwB5H7+2DSDP0Z5PmzZtkrfeesvU4ADI+8IDXQAA8DdtQ3Pq1ClZvHhxoIsCIJdQQwMAAKxHGxoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAILb7f7Kr2GDmxg+EAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#哪一天订单数量最多\n",
    "data['daycount'] =1 \n",
    "data['day'] = data['time'].map(lambda x:x.day)\n",
    "gp_by_day = data.groupby(by='day').count()['daycount']\n",
    "gp_by_day.plot(kind='bar')\n",
    "plt.xlabel('day')\n",
    "plt.ylabel('order number')\n",
    "plt.title('relationship between order number and  day')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2a389405-2c3f-4ec7-94de-f6982f0f3cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看星期几人数最多，订餐数最多\n",
    "data['weekcount'] = 1 \n",
    "data['week'] = data['time'].map(lambda x:x.weekday())\n",
    "gp_by_week = data.groupby(by='week').count()['weekcount']\n",
    "gp_by_week.plot(kind='bar')\n",
    "plt.xlabel('week')\n",
    "plt.ylabel('order number')\n",
    "plt.title('relationship between order number and  week')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python(Data_clean)",
   "language": "python",
   "name": "data_clean"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
